Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Regarding claim 1:
Step 2A Prong 1:
A method, comprising:
performing, with the generative model, a reverse phase process that removes noise from one or more training samples that were used to train the generative model; (Mathematical concept, math computations of a known generative algorithm.)
in conjunction with the reverse phase process, consulting, by the generative model, a guidance loss; and (Mental Process, simply referring to another value from the model can be done in the mind by observation and evaluation.)
Step 2A Prong 2:
providing an input to a generative model that was trained using a training dataset, and the input comprises a feature set of a quantum circuit; (Adding insignificant extra-solution activity to the judicial exception MPEP 2106.05(g) – Examiner’s note: merely sharing circuit data as an input to a model. Also training a generic generative model on generic data)
generating, based in part on the guidance loss, a set of samples, and one of the samples comprises a tensor network that represents the quantum circuit. (Adding insignificant extra-solution activity to the judicial exception MPEP 2106.05(g) – Examiner’s note: merely generating a response corresponding to the input.)
Step 2B:
providing an input to a generative model that was trained using a training dataset, and the input comprises a feature set of a quantum circuit; (Providing an input to a model is well understood routine and conventional activity that adds nothing meaningful to the exceptions, see MPEP 2106.05(d))
generating, based in part on the guidance loss, a set of samples, and one of the samples comprises a tensor network that represents the quantum circuit. (Generating sample data is well understood routine and conventional activity that adds nothing meaningful to the exceptions, see MPEP 2106.05(d))
Regarding claim 2:
The method as recited in claim 1, wherein the generative model is a diffusion model.
This provides a further description of the method discussed with regard to claim 1. The limitations of claim 2, under broadest reasonable interpretation, do not include new abstract ideas.
Claim 2 recites additional elements of adding insignificant extra-solution activity to the judicial exception MPEP 2106.05(g) – (Examiner’s note: merely mentions what model is used for generating data)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exceptions when taken alone or in combination with previous claims. The claimed
additional element is just insignificant extra-solution activity. Mentioning what generative model is used, when taken individually or in combination with previous additional elements, is well understood and routine conventional activity that adds nothing meaningful to the exceptions. Therefore, the judicial exceptions are not integrated into a practical application.
Claim 2 is ineligible.
Regarding claim 3:
The method as recited in claim 1, wherein the generative model was trained by a forward phase process comprising adding noise to the one or more training samples.
This provides a further description of the method discussed with regard to claim 1. The limitations of claim 3, under broadest reasonable interpretation, does include a new abstract idea of mathematical concepts. The forward process is also a known math computation of a known generative algorithm.
The claim does not include any additional elements that integrate the abstract idea into a practical application or amount to significantly more than the judicial exceptions, see MPEP 2106.05.
Claim 3 is ineligible.
Regarding claim 4:
The method as recited in claim 1, wherein the training dataset comprised a group of tuples (Tinput, C), where Tinput is a known representation of a tensor network, and C is a quantum circuit to which that representation corresponds.
This provides a further description of the method discussed with regard to claim 1. The limitations of claim 4, under broadest reasonable interpretation, do not include new abstract ideas.
Claim 4 recites additional elements of adding insignificant extra-solution activity to the judicial exception MPEP 2106.05(g) – (Examiner’s note: merely mentions what the training data is)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exceptions when taken alone or in combination with previous claims. The claimed
additional element is just insignificant extra-solution activity. Mentioning what training data is used for a generic generative model, when taken individually or in combination with previous additional elements, is well understood and routine conventional activity that adds nothing meaningful to the exceptions. Therefore, the judicial exceptions are not integrated into a practical application.
Claim 4 is ineligible
Regarding claim 5:
The method as recited in claim 1, wherein the tensor network is a contracted tensor network created from a group of tensor networks.
This provides a further description of the method discussed with regard to claim 1. The limitations of claim 5, under broadest reasonable interpretation, do not include new abstract ideas.
Claim 5 recites additional elements of adding insignificant extra-solution activity to the judicial exception MPEP 2106.05(g) – (Examiner’s note: merely mentions a property of the tensor networks without how compression was done or what effects it caused)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exceptions when taken alone or in combination with previous claims. The claimed
additional element is just insignificant extra-solution activity. Mentioning a property of the tensor network data without any viable effect to the invention, when taken individually or in combination with previous additional elements, is well understood and routine conventional activity that adds nothing meaningful to the exceptions. Therefore, the judicial exceptions are not integrated into a practical application.
Claim 5 is ineligible
Regarding claim 6:
The method as recited in claim 1, wherein the generative model comprises an encoder that uses the feature set as a basis to generate the guidance loss.
This provides a further description of the method discussed with regard to claim 1. The limitations of claim 6, under broadest reasonable interpretation, do not include new abstract ideas.
Claim 6 recites additional elements of adding insignificant extra-solution activity to the judicial exception MPEP 2106.05(g) – (Examiner’s note: merely mentions extra software that formats data with no specific effect)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exceptions when taken alone or in combination with previous claims. The claimed
additional element is just insignificant extra-solution activity. Mentioning a new software with no effects, when taken individually or in combination with previous additional elements, is well understood and routine conventional activity that adds nothing meaningful to the exceptions. Therefore, the judicial exceptions are not integrated into a practical application.
Claim 6 is ineligible
Regarding claim 7:
The method as recited in claim 1, wherein the reverse phase process is performed using Markov chains.
This provides a further description of the method discussed with regard to claim 1. The limitation of claim 7, under broadest reasonable interpretation, does include a new abstract idea of mathematical concepts. The disclosed Markov chains are a well-known mathematical structure and are used to implement the abstract idea of the reverse process.
The claim does not include any additional elements that integrate the abstract idea into a practical application or amount to significantly more than the judicial exceptions, see MPEP 2106.05.
Claim 7 is ineligible
Regarding claim 8:
The method as recited in claim 1, wherein the tensor network generated as part of one of the samples is a compressed version of the quantum circuit.
This provides a further description of the method discussed with regard to claim 1. The limitations of claim 8, under broadest reasonable interpretation, do not include new abstract ideas.
Claim 8 recites additional elements of adding insignificant extra-solution activity to the judicial exception MPEP 2106.05(g) – (Examiner’s note: merely specifies a property of the output data without disclosing how compression was achieved or the specific benefit of compression)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exceptions when taken alone or in combination with previous claims. The claimed
additional element is just insignificant extra-solution activity. Mentioning one of the properties of the generated output data without reason, when taken individually or in combination with previous additional elements, is well understood and routine conventional activity that adds nothing meaningful to the exceptions. Therefore, the judicial exceptions are not integrated into a practical application.
Claim 8 is ineligible
Regarding claim 9:
The method as recited in claim 1, wherein the reverse phase process is performed using a pure noisy input.
This provides a further description of the method discussed with regard to claim 1. The limitations of claim 9, under broadest reasonable interpretation, do not include new abstract ideas.
Claim 9 recites additional elements of adding insignificant extra-solution activity to the judicial exception MPEP 2106.05(g) – (Examiner’s note: merely specifies the input data for a process)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exceptions when taken alone or in combination with previous claims. The claimed
additional element is just insignificant extra-solution activity. Mentioning the input data is noisy, when taken individually or in combination with previous additional elements, is well understood and routine conventional activity that adds nothing meaningful to the exceptions. Therefore, the judicial exceptions are not integrated into a practical application.
Claim 9 is ineligible
Regarding claim 10:
The method as recited in claim 1, wherein the generative model is operable to generate the tensor network representative of the quantum circuit, using only the feature set of the quantum circuit as the input.
This provides a further description of the method discussed with regard to claim 1. The limitations of claim 10, under broadest reasonable interpretation, do not include new abstract ideas.
Claim 10 recites additional elements of adding insignificant extra-solution activity to the judicial exception MPEP 2106.05(g) – (Examiner’s note: merely mentions one possible input or base case with no effect on the invention)
The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exceptions when taken alone or in combination with previous claims. The claimed
additional element is just insignificant extra-solution activity. Mentioning a single case of the input, when taken individually or in combination with previous additional elements, is well understood and routine conventional activity that adds nothing meaningful to the exceptions. Therefore, the judicial exceptions are not integrated into a practical application.
Claim 10 is ineligible
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-7. 8-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Fürrutter et al. (“Quantum circuit synthesis with diffusion models”) in view of Dhand et al. (US 20220391571 A1).
Regarding claim 1:
Fürrutter teaches:
A method, comprising:
providing an input to a generative model that was trained using a training dataset, and the input comprises a feature set of a quantum circuit;
[Col 4, Par 1, Lin 6-10] teaches an input. The input is used in a Diffusion Model [Col 3, Lines 2-4] which is a generative model. [Col 3, Par 3, Lin 1-5] teaches training the model on a training set of data. Appendix B [Col 16-17] also teaches creating a training dataset.
performing, with the generative model, a reverse phase process that removes noise from one or more training samples that were used to train the generative model;
Fig 1D and [Col 3, Par 3, Lin 3-5] teaches iterative denoising which removes noise from the training dataset.
in conjunction with the reverse phase process, consulting, by the generative model, a guidance loss; and
Fig 1C and 1D teach training the model and making inferences happening together. Appendix D [Col 21, Par 1-2] teaches the training of the model and mentions a classifier-free-guidance score. Appendix E [Col 24, Para 1-2] teaches inferencing, which happens together with training, and uses the classifier-free-guidance score as a guidance loss value to guide the model. This guidance
generating, based in part on the guidance loss, a set of samples, and one of the samples comprises a tensor … that represents the quantum circuit.
[Col 16, Par 3] teaches decoding of continuous tensors that the model generated. These tensors are representative of whole quantum circuits. [Col 9, Para 2, Lin 6] teaches generating a set of circuits which we know are each represented by a tensor.
Fürrutter doesn’t teach “tensor network”
Dhand does teach “tensor network” ([0004] teaches a contraction tree being a representation of a quantum circuit. These trees are tensor networks. [0021] also teaches using tensor networks as a fast method for simulating quantum circuits.)
It would have been obvious to for one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Fürrutter’s teaching of circuit tensors with Dhand’s teaching of tensor networks representing a circuit. This would allow for more efficient simulation of larger quantum circuits because a tensor network can represent multiple tensors connected together rather than compressing an entire circuit into one tensor that’s limited in size.
Regarding claim 2, Fürrutter teaches:
The method as recited in claim 1, wherein the generative model is a diffusion model.
For the same reasons as claim 1, [Col 3, Lines 2-4] teaches using a Diffusion Model.
Regarding claim 3, Fürrutter teaches:
The method as recited in claim 1, wherein the generative model was trained by a forward phase process comprising adding noise to the one or more training samples.
[Col 3, Par 3, Lin 1-3] teaches adding noise to the samples in the training set. Fig 1 also illustrates the process of adding noise to the dataset.
Regarding claim 4:
Fürrutter teaches:
The method as recited in claim 1, wherein the training dataset comprised a group of tuples (Tinput, C), where Tinput is a known representation of a tensor …, and C is a quantum circuit to which that representation corresponds.
Fig 3, [Col 7, Par 1] and [Col 8, Par 2, Lin 10-13] and [Col 8, Par 3, Lin 4-8] teaches grouping Tensors and Quantum Circuits together with specific processes like masking and editing. Masking and editing fix certain gates in a circuit and provide constraints on a circuit. Doing so creates tensors and quantum circuits that correspond to each other and were defined or known by the user.
Fürrutter does not teach “tensor network”.
Dhand teaches “tensor network” (For the same reasons as claim 1, [0004] and [0021] teach tensor networks).
Regarding claim 5:
Fürrutter doesn’t teach The method as recited in claim 1, wherein the tensor network is a contracted tensor network created from a group of tensor networks.
Dhand does teach The method as recited in claim 1, wherein the tensor network is a contracted tensor network created from a group of tensor networks. ([0004] teaches a tensor network from a set of networks called a contraction tree. [0024] teaches tree decomposition and graph partitioning as methods for creating a contracted tensor network. Fig 5 is a contracted tensor network for the contraction tree taught by Fig 3.)
Regarding claim 6, Fürrutter teaches:
The method as recited in claim 1, wherein the generative model comprises an encoder that uses the feature set as a basis to generate the guidance loss.
[Col 3, Par 2, Lin 5 – 7] teaches the guidance loss or guiding metric being affected by the Diffusion Models generation condition. [Col 3, Par 2, Lin 12 – 21] and Fig 1B. teach the condition is made from an encoder for text promptings along with an encoder for unitary values. The unitaries and text prompts make up the input for the generative model.
Regarding claim 8:
Fürrutter doesn’t teach The method as recited in claim 1, wherein the tensor network generated as part of one of the samples is a compressed version of the quantum circuit.
Dhand does teach The method as recited in claim 1, wherein the tensor network generated as part of one of the samples is a compressed version of the quantum circuit. ([0024] and [0027] teaches tensor networks as a representation of a quantum circuit. The tensor networks can be contracted to become smaller and simulate the time complexity of the quantum circuit. A contracted tensor network would therefore be a compressed version of a quantum circuit.)
Regarding claim 9, Fürrutter teaches:
The method as recited in claim 1, wherein the reverse phase process is performed using a pure noisy input.
Fig 1c and 1d teaches the reverse phase process using a noisy dataset in training and a pure noise dataset in inferencing. The denoising section of the inference process uses pure noisy data as input.
Regarding claim 10,
Fürrutter teaches:
The method as recited in claim 1, wherein the generative model is operable to generate the tensor … representative of the quantum circuit, using only the feature set of the quantum circuit as the input.
[Col 4, Par 3], [Col 5, Par 1], and [Col 5, Par 2 – Col 6, Par 1, Line 5] teach the Diffusion Model generating continuous tensors corresponding to circuits made only from a feature set of gate types and qubit numbers.
Fürrutter doesn’t teach “tensor network”
Dhand teaches “tensor network” (For the same reasons as claim 1, [0004] and [0021] teach tensor networks).
Claims 11 – 16 and 18 – 20 are machine claims with the same limitations as claims 1 – 6 and 8 – 10 and are rejected for the same reasons.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Fürrutter et al. (“Quantum circuit synthesis with diffusion models”) in view of Dhand et al. (US 20220391571 A1) in view of Ho et al. (“Denoising Diffusion Probabilistic Models”).
Regarding claim 7:
Fürrutter over Dhand doesn’t teach The method as recited in claim 1, wherein the reverse phase process is performed using Markov chains.
Ho teaches The method as recited in claim 1, wherein the reverse phase process is performed using Markov chains. ([Page 2, Par 1] and [Page 2, Par 4] teaches using Markov Chains in the reverse process of a Diffusion Model)
It would have been obvious to for one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Fürrutter’s teaching of a reverse process for a diffusion model with Ho’s teaching of using Markov chains to do so since Fürrutter directly references Ho’s teaching in reference 49. This discloses the specifics of a reverse process for a diffusion probabilistic model.
Regarding claim 17:
Claims 17 is a machine claim reciting the same limitations as Claim 7 and are rejected for the same reasons.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AJAY J KANJOOR whose telephone number is (571)270-0965. The examiner can normally be reached Monday-Friday 8am-4pm.
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/AJAY J KANJOOR/Examiner, Art Unit 2142
/Mariela Reyes/Supervisory Patent Examiner, Art Unit 2142
